140.754.41
Advanced Methods in Biostatistics IV

Prerequisite:

140.751-753

Description:

Extends topics in 140.753 to encompass generalized linear mixed effects models. Introduces expectation-maximization and Markov Chain Monte Carlo. Introduces functional data analysis. Foundational topics include: linear mixed model, generalized linear mixed model, EM, MCMC, models for longitudinal data, and functional data analysis. Emphasizes both rigorous methodological development and practical data analytic strategies. Discusses the role of quantitative methods and sciences in public health, including how to use them to describe and assess population health, and the critical importance of evidence in advancing public health knowledge.

Learning Objectives:

Upon successfully completing this course, students will be able to:

1. Use modern statistical concepts such as linear mixed model (LMM) and generalized linear mixed models (GLMM) for inference
2. Describe the relationship between LMM and GLMM
3. Extend models to account for clustering and correlation
4. Understand and use EM and MCMC
5. Learn techniques for solving prediction problems
6. Describe modern statistical methods for complex datasets
7. Improve computational and analytic skills through analysis of simulated and real data sets
8. Explain the role of quantitative methods and sciences in describing and assessing a population’s health
9. Explain the critical importance of evidence in advancing public health knowledge

Methods of Assessment:

This course is evaluated as follows:

• 60% Homework
• 40% Final Exam

Instructor Consent:

No consent required

Special Comments:

Please note: This is the virtual/online section of a course that is also offered onsite. Students will need to commit to the modality for which they register.